Paper Title :Learning Pushing and Grasping Via Deep Reinforcement Learning and Grasping Potential Module
Author :Yu-Hsiangshao, Chih-Yung Huang, Ying-Chun Wang
Article Citation :Yu-Hsiangshao ,Chih-Yung Huang ,Ying-Chun Wang ,
(2024 ) " Learning Pushing and Grasping Via Deep Reinforcement Learning and Grasping Potential Module " ,
International Journal of Mechanical and Production Engineering (IJMPE) ,
pp. 1-7,
Volume-12,Issue-4
Abstract : Robotic arms are often used for grasping objects, but grasping in cluttered scenarios may lead to collisions due to
insufficient space. Pushing can help scatter cluttered objects to make space for robotic arm hence increasing the grasp
success rate. In this paper, we propose a method that combines deep reinforcement learning with instance segmentation
networks to achieve collaborative pushing and grasping. In our work, we use a grasping potential module to estimate the
grasp potential of different objects in the environment.The agent prioritizes grasping high-potential objects, allowing it to
quickly learn the pushing and grasping policies in complex environments. Additionally, a novel pushing strategy is proposed
to prevent ineffective pushing. Experimental results validate that our method achieves a 93% grasp success rate and
converges faster than other baseline models.
Keywords - Deep Reinforcement Learning, Instance Segmentation, Grasping
Type : Research paper
Published : Volume-12,Issue-4
DOIONLINE NO - IJMPE-IRAJ-DOIONLINE-20720
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Copyright: © Institute of Research and Journals
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Published on 2024-07-11 |
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